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This repository contains code used to train U-Net on a multi-class segmentation dataset.

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This repository contains code used to train U-Net on a multi-class segmentation dataset. The code has been written in python. Inside the scripts folder, you can find all the different python files used to train, evaluate and prepare the data. The dataset that was used is the cityscapes dataset. If you want to know how to perform image segmentation tasks in PyTorch, then visit my text-based tutorial on medium.

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Dependencies

Sl. no. Dependency Install
1. PyTorch Head on to PyTorch's official website and choose your OS, cuda version and other specifications and install using the generated terminal command.
2. torchvision pip install torchvision
3. Pillow python3 -m pip install --upgrade Pillow
4. NumPy pip install numpy
5. CityscapesScripts python -m pip install cityscapesscripts

There are some other libraries that were also used such as Matplotlib (for visualization) and TQDM (to show the progress bar), but because they were only supplementary, I did not include them in the table above.

About

This repository contains code used to train U-Net on a multi-class segmentation dataset.

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